reijii/klippain-shaketune-telegramm
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Updated the scripts and improved vibration graphs

Browse Source 
Full revamped documentation
This commit is contained in:
Félix Boisselier
2023-10-19 23:45:11 +02:00
parent 10cf8d3566
commit 33ce0ada85
53 changed files with 445 additions and 128 deletions
Download Patch File Download Diff File Expand all files Collapse all files

78
K-ShakeTune/scripts/graph_belts.py
View File

@@ -4,9 +4,10 @@
######## CoreXY BELTS CALIBRATION SCRIPT ########
#################################################
# Written by Frix_x#0161 #
# @version: 1.0
# @version: 2.0
# CHANGELOG:
# v2.0: updated the script to align it to the new K-Shake&Tune module
# v1.0: first version of this tool for enhanced vizualisation of belt graphs
@@ -33,7 +34,6 @@ except locale.Error:
locale.setlocale(locale.LC_TIME, 'C')
MAX_TITLE_LENGTH = 65
ALPHABET = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" # For paired peaks names
PEAKS_DETECTION_THRESHOLD = 0.20
@@ -91,7 +91,9 @@ def compute_curve_similarity_factor(signal1, signal2):
def detect_peaks(psd, freqs, window_size=5, vicinity=3):
# Smooth the curve using a moving average to avoid catching peaks everywhere in noisy signals
kernel = np.ones(window_size) / window_size
smoothed_psd = np.convolve(psd, kernel, mode='same')
smoothed_psd = np.convolve(psd, kernel, mode='valid')
mean_pad = [np.mean(psd[:window_size])] * (window_size // 2)
smoothed_psd = np.concatenate((mean_pad, smoothed_psd))
# Find peaks on the smoothed curve
smoothed_peaks = np.where((smoothed_psd[:-2] < smoothed_psd[1:-1]) & (smoothed_psd[1:-1] > smoothed_psd[2:]))[0] + 1
@@ -297,12 +299,12 @@ def combined_spectrogram(data1, data2):
return combined_data, bins1, t1
# Compute a composite and highly subjective value indicating the "chance of mechanical issues on the printer (0 to 100%)"
# that is based on the differential spectrogram sum of gradient salted with a bit of the estimated similarity cross-correlation
# from compute_curve_similarity_factor() and with a bit of the number of unpaired peaks.
# Compute a composite and highly subjective value indicating the "mechanical health of the printer (0 to 100%)" that represent the
# likelihood of mechanical issues on the printer. It is based on the differential spectrogram sum of gradient, salted with a bit
# of the estimated similarity cross-correlation from compute_curve_similarity_factor() and with a bit of the number of unpaired peaks.
# This result in a percentage value quantifying the machine behavior around the main resonances that give an hint if only touching belt tension
# will give good graphs or if there is a chance of mechanical issues in the background (above 50% should be considered as probably problematic)
def compute_comi(combined_data, similarity_coefficient, num_unpaired_peaks):
def compute_mhi(combined_data, similarity_coefficient, num_unpaired_peaks):
filtered_data = combined_data[combined_data > 100]
# First compute a "total variability metric" based on the sum of the gradient that sum the magnitude of will emphasize regions of the
@@ -321,7 +323,23 @@ def compute_comi(combined_data, similarity_coefficient, num_unpaired_peaks):
# Ensure the result lies between 0 and 100 by clipping the computed value
final_percentage = np.clip(final_percentage, 0, 100)
return final_percentage
return final_percentage, mhi_lut(final_percentage)
# LUT to transform the MHI into a textual value easy to understand for the users of the script
def mhi_lut(mhi):
if 0 <= mhi <= 30:
return "Excellent mechanical health"
elif 31 <= mhi <= 45:
return "Good mechanical health"
elif 46 <= mhi <= 55:
return "Acceptable mechanical health"
elif 56 <= mhi <= 70:
return "Potential signs of a mechanical issue"
elif 71 <= mhi <= 85:
return "Likely a mechanical issue"
elif 86 <= mhi <= 100:
return "Mechanical issue detected"
######################################################################
@@ -361,7 +379,7 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq):
label = ALPHABET[paired_peak_count]
amplitude_offset = abs(((signal2.psd[peak2[0]] - signal1.psd[peak1[0]]) / max(signal1.psd[peak1[0]], signal2.psd[peak2[0]])) * 100)
frequency_offset = abs(signal2.freqs[peak2[0]] - signal1.freqs[peak1[0]])
offsets_table_data.append([f"Peaks {label}", f"{frequency_offset:.2f} Hz", f"{amplitude_offset:.2f} %"])
offsets_table_data.append([f"Peaks {label}", f"{frequency_offset:.1f} Hz", f"{amplitude_offset:.1f} %"])
ax.plot(signal1.freqs[peak1[0]], signal1.psd[peak1[0]], "x", color='black')
ax.plot(signal2.freqs[peak2[0]], signal2.psd[peak2[0]], "x", color='black')
@@ -369,28 +387,29 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq):
ax.annotate(label + "1", (signal1.freqs[peak1[0]], signal1.psd[peak1[0]]),
textcoords="offset points", xytext=(8, 5),
ha='left', fontsize=14, color='black')
ha='left', fontsize=13, color='black')
ax.annotate(label + "2", (signal2.freqs[peak2[0]], signal2.psd[peak2[0]]),
textcoords="offset points", xytext=(8, 5),
ha='left', fontsize=14, color='black')
ha='left', fontsize=13, color='black')
paired_peak_count += 1
for peak in signal1.unpaired_peaks:
ax.plot(signal1.freqs[peak], signal1.psd[peak], "x", color='black')
ax.annotate(str(unpaired_peak_count + 1), (signal1.freqs[peak], signal1.psd[peak]),
textcoords="offset points", xytext=(8, 5),
ha='left', fontsize=14, color='red', weight='bold')
ha='left', fontsize=13, color='red', weight='bold')
unpaired_peak_count += 1
for peak in signal2.unpaired_peaks:
ax.plot(signal2.freqs[peak], signal2.psd[peak], "x", color='black')
ax.annotate(str(unpaired_peak_count + 1), (signal2.freqs[peak], signal2.psd[peak]),
textcoords="offset points", xytext=(8, 5),
ha='left', fontsize=14, color='red', weight='bold')
ha='left', fontsize=13, color='red', weight='bold')
unpaired_peak_count += 1
# Compute the similarity (using cross-correlation of the PSD signals)
ax2 = ax.twinx() # To split the legends in two box
ax2.yaxis.set_visible(False)
similarity_factor = compute_curve_similarity_factor(signal1, signal2)
ax2.plot([], [], ' ', label=f'Estimated similarity: {similarity_factor:.1f}%')
ax2.plot([], [], ' ', label=f'Number of unpaired peaks: {unpaired_peak_count}')
@@ -415,7 +434,7 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq):
# Print the table of offsets ontop of the graph below the original legend (upper right)
if len(offsets_table_data) > 0:
columns = ["", "Frequency delta", "Amplitude delta", ]
offset_table = ax.table(cellText=offsets_table_data, colLabels=columns, bbox=[0.67, 0.70, 0.3, 0.2], loc='upper right', cellLoc='center')
offset_table = ax.table(cellText=offsets_table_data, colLabels=columns, bbox=[0.66, 0.75, 0.33, 0.15], loc='upper right', cellLoc='center')
offset_table.auto_set_font_size(False)
offset_table.set_fontsize(8)
offset_table.auto_set_column_width([0, 1, 2])
@@ -434,13 +453,13 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq):
def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_factor, max_freq):
combined_data, bins, t = combined_spectrogram(data1, data2)
# Compute the COMI value from the differential spectrogram sum of gradient, salted with
# Compute the MHI value from the differential spectrogram sum of gradient, salted with
# the similarity factor and the number or unpaired peaks from the belts frequency profile
# Be careful, this value is highly opinionated and is pretty experimental!
comi = compute_comi(combined_data, similarity_factor, len(signal1.unpaired_peaks) + len(signal2.unpaired_peaks))
print(f"Chances of mechanical issues: {comi:.1f}%")
ax.set_title(f"Differential Spectrogram (COMI: {comi:.1f}%)", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.plot([], [], ' ', label=f'Chances of mechanical issues (COMI): {comi:.1f}%')
mhi, textual_mhi = compute_mhi(combined_data, similarity_factor, len(signal1.unpaired_peaks) + len(signal2.unpaired_peaks))
print(f"[experimental] Mechanical Health Indicator: {textual_mhi.lower()} ({mhi:.1f}%)")
ax.set_title(f"Differential Spectrogram", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
ax.plot([], [], ' ', label=f'{textual_mhi} (experimental)')
# Draw the differential spectrogram with a specific norm to get light grey zero values and red for max values (vmin to vcenter is not used)
norm = matplotlib.colors.TwoSlopeNorm(vcenter=np.min(combined_data), vmax=np.max(combined_data))
@@ -550,19 +569,26 @@ def belts_calibration(lognames, klipperdir="~/klipper", max_freq=200.):
ax1 = fig.add_subplot(gs[0])
ax2 = fig.add_subplot(gs[1])
# Add title
filename = lognames[0].split('/')[-1]
dt = datetime.strptime(f"{filename.split('_')[1]} {filename.split('_')[2]}", "%Y%m%d %H%M%S")
title_line1 = "RELATIVE BELT CALIBRATION TOOL"
title_line2 = dt.strftime('%x %X')
fig.text(0.88, 0.965, title_line1, ha='right', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold')
fig.text(0.88, 0.957, title_line2, ha='right', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
fig.text(0.12, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold')
fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
# Plot the graphs
similarity_factor, _ = plot_compare_frequency(ax1, lognames, signal1, signal2, max_freq)
plot_difference_spectrogram(ax2, datas[0], datas[1], signal1, signal2, similarity_factor, max_freq)
fig.set_size_inches(10, 13)
fig.set_size_inches(8.3, 11.6)
fig.tight_layout()
fig.subplots_adjust(top=0.90)
fig.subplots_adjust(top=0.89)
# Adding a small Klippain logo to the top left corner of the figure
ax_logo = fig.add_axes([0.001, 0.899, 0.1, 0.1], anchor='NW', zorder=-1)
ax_logo.imshow(matplotlib.pyplot.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
ax_logo.axis('off')
return fig
@@ -584,12 +610,6 @@ def main():
opts.error("You must specify an output file.png to use the script (option -o)")
fig = belts_calibration(args, options.klipperdir, options.max_freq)
# Adding a small Klippain logo to the top left corner of the figure
ax_logo = fig.add_axes([0.899, 0.899, 0.1, 0.1], anchor='NE', zorder=-1)
ax_logo.imshow(matplotlib.pyplot.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
ax_logo.axis('off')
fig.savefig(options.output)